Senior Java Software Engineer Jobs in McKinney, TX

Location: Irving, TX Salary: Negotiable Description: Our client is currently seeking a Senior Java Developer (AI-Augmented Engineering)
Job Title: Senior Java Developer (AI-Augmented Engineering)
Location: Irving TX or Basking Ridge, NJ
Working Model: Hybrid- 3 days in office, Tues, Wed, Thurs
Job Summary
We are seeking a forward-thinking, highly skilled Senior Java Developer to join our engineering team. In this role, you will not only design, build, and maintain robust, scalable enterprise applications, but you will also champion the next generation of software development by deeply integrating AI collaboration tools into your daily workflow.
The ideal candidate is an expert in the Java ecosystem who treats AI assistants-such as GitHub Copilot, Claude, and Gemini-as force multipliers. You should possess strong prompt engineering skills, enabling you to accelerate development velocity, optimize code quality, automate testing, and solve complex architectural challenges efficiently.
Key Responsibilities
Core Java Development: Design, develop, and maintain high-performance, scalable, and secure microservices and enterprise applications using modern Java versions (Java 17/21) and the Spring Boot framework.
AI-Assisted Engineering: Seamlessly integrate AI coding assistants (GitHub Copilot, Claude, Gemini) into the software development lifecycle (SDLC) to write clean code, refactor legacy systems, and generate documentation.
Advanced Prompt Engineering: Formulate, test, and refine advanced prompts (system-level, few-shot, and chained prompting) to extract high-context, syntactically correct, and secure code outputs from Large Language Models (LLMs).
Code Quality & Review: Oversee the review of both human-written and AI-generated code, ensuring adherence to design patterns, security standards, performance benchmarks, and company best practices.
Testing & Automation: Utilize AI tools to rapidly generate robust unit, integration, and regression test suites (using JUnit, Mockito, etc.), significantly improving code coverage.
Troubleshooting & Optimization: Leverage LLMs to expedite root-cause analysis of production bugs, decipher complex stack traces, and identify performance bottlenecks.
Continuous Learning: Stay at the forefront of evolving AI technologies and software engineering trends, sharing insights and prompt libraries with the broader development team.
Required Skills & Qualifications
Technical Architecture & Java Ecosystem
Experience: Professional experience in enterprise software development using Java (Java 11 or higher, Java 17+ preferred).
Frameworks: Extensive hands-on experience with Spring Boot, Spring Cloud, and Hibernate/JPA.
Microservices: Strong understanding of microservices architecture, RESTful API design, and event-driven patterns.
Databases: Proficiency with relational databases (e.g., PostgreSQL, MySQL) and NoSQL solutions (e.g., MongoDB, Redis).
CI/CD & DevOps: Solid experience with Git, Docker, Kubernetes, and building automated pipelines (Jenkins, GitHub Actions, or GitLab CI).
AI & Prompt Engineering Competencies
AI Tool Fluency: Daily operational experience using GitHub Copilot (or Copilot Chat), Claude, and Gemini to accelerate development.
Prompt Engineering Mastery: Demonstrated ability to construct precise prompts, provide sufficient context, and use specialized techniques to reduce AI hallucinations and maximize code relevance.
Validation & Security: A critical mindset capable of verifying AI-generated logic for potential security vulnerabilities (such as OWASP Top 10 flaws), licensing risks, and inefficiencies.
Soft Skills & Attributes
Critical Thinking: Strong analytical skills with the ability to question and validate AI-generated outputs rather than blindly accepting them.
Adaptability: A growth mindset with an eagerness to adapt to rapidly changing AI tools and development paradigms.
Collaboration & Communication: Ability to articulate technical requirements clearly to both human teammates and AI agents.
Preferred Qualifications
Experience building or integrating custom AI plugins or internal developer tools.
Familiarity with cloud platforms (AWS, Google Cloud Platform, or Azure).
Active contribution to open-source projects or a portfolio demonstrating creative use of AI in software development.
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